Quantization and Coding

This chapter discusses principles of scalar quantization and explains the operation of a vector quantization. It explores the principles of minimum‐redundancy code word assignment and provides the important algorithm classes for lossless. The chapter also explains several image compression approaches, including the Discrete Cosine Transform. It helps the reader to understand the basic approach to waveform and parametric speech encoding and then explains the advantages and disadvantages of each. The chapter also explores the key requirements for audio encoders and the building blocks that go to make up an audio encoding system. It reviews some of the notions of probability, which are useful in modeling errors in communication channels, as well as difference equations, which are used extensively in signal encoding. The chapter gives an overview of coding: Image Coding; Source Coding; and Speech and Audio Coding. It also discusses digital channel capacity.